we evaluate our method on the challenging PASCAL VOC2007 datasets[1]} which have 9963 images for 20 object classes and are divided into three subsets: training, verification, and testing sets. The trainval set (5011 images) is chosen to train our proposed network. As we focus on weakly-supervised detection, only image-level labels (presence or absence of a class in the chosen image)are utilized during training. For testing, we evaluating our network using mean average precision (mAP)[1]} and correct localization (CorLoc) metrics[3]}. All these metrics are based on the PASCAL criteria of IoU > 0.5 between ground truths and predicted boxes.
